Description for Button Statistics for Discriminant analysis and the output:
Discriminant Analysis is used primarily to predict membership in two or more mutually exclusive groups. To analyse Discriminant use menu Analyze -> Classify->Discriminant
First enter the grouping variable (here: variable category). Then, define the lowest and highest coded value
for the grouping variable by clicking on Button Define Range. For example if the variable category has only two levels you enter 1 and 2 in the boxes. See the second figure standing above. Then, select the independent variables in the ‘Independents:’ box.
There are several methods for discriminant analyses, but here we will only use ‘Enter independents
together’, which is standard selected.
let see all Button Statistics…
Here you can indicate those statistics that are desired in discriminant analysis. Often these include:
Means: The means and standard deviations for each variable for each group (the two levels of category in
this case), and for the entire sample.
Univariate ANOVAs: This compares the mean values for each group for each variable to see if there are
significant univariate differences between means.
Box’s M: A test for the equality of the group covariance matrices. For sufficiently large samples, a nonsignificant p value means there is insufficient evidence that the matrices differ. The test is sensitive to
departures from multivariate normality.
Unstandardized Function Coefficients: The unstandardized coefficients of the discriminant equation
based on the raw scores of discriminating variables.Button Classify…
Many classification options can be selected here, such as prior probabilities and plots. Also, a summary
table can be requested.
Button Save:
This option allows you to save as new variables: Predicted group membership, Discriminant Scores and
Probabilities of group membership.
Output of Canonical Discriminant Functions contains:
Eigenvalue: An eigenvalue indicates the proportion of variance explained. (Between-groups sums of squares divided by within-groups sums of squares). A large eigenvalue is associated with a strong function.
The 'Canonical Discriminant Function Coefficients’‘ indicate the unstandardised scores concerning the independent variables. It is the list of coefficients of the unstandardised discriminant equation.
‘Functions at Group Centroide’ indicates the average discriminant score.
‘Classification Results’ is a simple summary of number and percent of our selected values.
Discriminant Analysis is used primarily to predict membership in two or more mutually exclusive groups. To analyse Discriminant use menu Analyze -> Classify->Discriminant
First enter the grouping variable (here: variable category). Then, define the lowest and highest coded value
for the grouping variable by clicking on Button Define Range. For example if the variable category has only two levels you enter 1 and 2 in the boxes. See the second figure standing above. Then, select the independent variables in the ‘Independents:’ box.
There are several methods for discriminant analyses, but here we will only use ‘Enter independents
together’, which is standard selected.
let see all Button Statistics…
Here you can indicate those statistics that are desired in discriminant analysis. Often these include:
Means: The means and standard deviations for each variable for each group (the two levels of category in
this case), and for the entire sample.
Univariate ANOVAs: This compares the mean values for each group for each variable to see if there are
significant univariate differences between means.
Box’s M: A test for the equality of the group covariance matrices. For sufficiently large samples, a nonsignificant p value means there is insufficient evidence that the matrices differ. The test is sensitive to
departures from multivariate normality.
Unstandardized Function Coefficients: The unstandardized coefficients of the discriminant equation
based on the raw scores of discriminating variables.Button Classify…
Many classification options can be selected here, such as prior probabilities and plots. Also, a summary
table can be requested.
Button Save:
This option allows you to save as new variables: Predicted group membership, Discriminant Scores and
Probabilities of group membership.
Output of Canonical Discriminant Functions contains:
Eigenvalue: An eigenvalue indicates the proportion of variance explained. (Between-groups sums of squares divided by within-groups sums of squares). A large eigenvalue is associated with a strong function.
The 'Canonical Discriminant Function Coefficients’‘ indicate the unstandardised scores concerning the independent variables. It is the list of coefficients of the unstandardised discriminant equation.
‘Functions at Group Centroide’ indicates the average discriminant score.
‘Classification Results’ is a simple summary of number and percent of our selected values.
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